class_docstr: A keras Layer, which intercepts its inputs and stored them as pickled.
class_docstr_tok: ["A","keras","Layer",",","which","intercepts","its","inputs","and","stored","them","as","pickled","."]
code: def __init__(self, num_outputs, **kwargs):
      super().__init__(**kwargs)
  
      self.dense = tf.keras.layers.Dense(
          units=num_outputs, kernel_initializer=normc_initializer(0.01)
      )
  
file_path: ray/python/ray/rllib/examples/models/rnn_spy_model.py
imports: import numpy
  import pickle
  import ray
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.misc import normc_initializer
  from ray.rllib.models.tf.recurrent_net import RecurrentNetwork
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
label_desc: Use image node_id 1 to create a new SpyLayer object from inherited base classes: tf.keras.layers with example: obj = SpyLayer(num_outputs)